7/22/2010 @ 6:00AM

Google Translate Tangles With Computer Learning

Ask most computer programmers what would happen if, suddenly, their computers got a thousand times faster. Most would rhapsodize about being able to immediately put that extra power to good use.

Ask Franz Josef Och the same question, though, and he says that even with a machine a thousand times more powerful than today’s his program wouldn’t run significantly better than it does right now, as far as most people could tell. Which is quite an admission, because Och is responsible for one of the most amazing computer programs in the world: He is head of the division at
Google
that runs Google Translate.

Google Translate is a free Google service that translates between scores of languages almost as quickly as Google returns search results. Give it a Web page from a Spanish newspaper and in about a second the text is converted to English. When you first encounter it, there is a whiff of sci-fi make-believe about this tearing down of ancient language barriers.

That, at least, is the impression you get as a new user. Spend more time with the software, though, and you realize while Google Translate is often spot-on perfect, especially with simple sentences from structurally simple languages like Spanish, it can produce puzzling sentences. Frequently the system has the fluency of a barely competent human translator, one who happens to be both distracted and drunk.

Here, for example, is a sentence from a World Cup report from a Madrid newspaper: “Passing Switzerland, Spain is now in the hands of the video and suture.”

I use Google Translate almost every day, usually to see what the rest of the world is thinking. It’s as good as it is because of advances in statistical translation. This method involves not parsing sentences the way you learned to do in grade school but rather comparing lots of phrases with their existing translations (which Google did by downloading stacks of translated documents, like those from the United Nations). Computer scientists have wrung nearly all of the performance they can from this approach and can’t do much better than they are, even as hardware continues to get faster.

In other words, Google Translate readers in the year 2020 will be able to find clunkers and non sequiturs almost as easily as they can today. A computer that translates as well as a human, which is as good a definition as any of genuine artificial intelligence, is nowhere in sight. Improvements to Google Translate are being made on a regular basis. But they are relatively small, incremental steps. “The trajectory we are on just doesn’t seem likely to reach artificial intelligence,” Och says.

This isn’t just an issue for computer translation; it exists for just about all of the tough problems computers are working in: language, vision, robotics–even search, which is at the heart of the Web. Computers have become remarkably useful but are still a far cry from the dream of early computer pioneers of a machine so smart it would be indistinguishable from a human.

Will one ever be? I am doubtful, though Och doesn’t share my pessimism, holding out for some breakthrough, even if he has no idea what it might look like. What’s needed, he says, are computer programs that learn how to learn things. Programmers will also need to figure out how to synthesize the intelligence of different programs. Right now a computer might play Jeopardy, but it knows nothing about playing chess. Neither the Jeopardy-playing computer nor the chess-playing one has any sense of how a soccer ball bounces. A computer would have to know a little bit about a lot of things to translate gracefully.

There are two kinds of engineers in Silicon Valley. A surprisingly large number believe, despite evidence to the contrary all around them, that computers are progressing so rapidly that we’re not far away from having a real-life version of HAL from the movie 2001: A Space Odyssey. Many of them happen to be colleagues of Och’s at Google, adherents of what’s called the Singularity Movement. It’s common in these circles to believe computers are becoming so much like people that, in another few decades, we will be able to download our consciousness into them and thus live forever.

The other sort of engineer understands that glib comparisons between computers and humans don’t do justice to the complexities of either. I’d put Och in that category. Guess which of them writes more useful software?